Why ML Models Need to be Monitored After Deploying
Considering that AI and ML applications are increasingly becoming a part of business operations, an important question pops up that cannot be ignored. Can machine learning models make mistakes? After all, ML models are meant to boost productivity, reduce errors, and mimic human decision-making. However, can it inadvertently replicate human inclinations for bias and errors? In simple terms, it can. This highlights the critical need to include an ML model monitoring framework in the development and deployment of reliable, impartial, and accountable AI applications.
What is Machine Learning Model Deployment?
Model deployment in machine learning refers to the process of taking a trained machine learning model (from the development environment) and making it available for use in a real-world, operational environment. This involves deploying the model to serve predictions or inferences based on new, incoming data.
Why is it important to include ML model monitoring after deployment?
Machine learning models can work accurately and exceed business expectations. However, mistakes might arise with time due to shifts in data patterns, unaccounted biases in training data, and unforeseen interactions with real-world inputs. These errors often occur when the model encounters data that it hasn’t seen before or when the model’s assumptions no longer hold in a changing environment. Continuous monitoring and adaptation are crucial to mitigate these issues.
Once you’ve deployed a machine learning model, it’s vital to keep a close watch on it. You should set up robust logging and monitoring systems to track how the model’s API is used, its performance, and any potential errors. For instance, monitor the types and formats of data sent to the API, ensuring it conforms to the model’s expectations.
Regular performance assessments help catch issues early, allowing you to make necessary adjustments. It’s also essential to ensure the model can adapt by seamlessly integrating new model versions and enhancing the API’s capacity to handle increased traffic. These proactive steps ensure that your machine learning model deployment is successful, consistently providing accurate predictions in real-world scenarios.
The goals for ML model monitoring are:
1. Detect problems with your model and the system serving your model in production before they start to generate negative business value.
2. Take action by triaging and troubleshooting models in production or the inputs and systems that enable them.
3. Ensure their predictions and results can be explained and reported.
4. Ensure the model’s prediction process is transparent to relevant stakeholders for proper governance.
5. Provide a path for maintaining and improving the model in production.
Key points to consider in ML model deployment?
Serving Infrastructure: Deployed models require a serving infrastructure that can handle incoming data, make predictions, and return results to applications or users. This infrastructure typically involves web servers or specialized inference services. It also includes API endpoints that allow applications to send data for prediction and receive the model’s output.
Scalability: Model deployment must accommodate varying workloads. It should be able to scale horizontally to handle increased traffic and maintain low-latency response times.
Model Format: Ensure that the model is in a suitable format for deployment, such as TensorFlow’s SavedModel or ONNX. This format should be optimized for inference and compatible with the deployment framework.
Data Drift: Continuously monitor the incoming data for data drift, which refers to changes in the statistical properties of the input data over time. Detecting data drift is important because it can impact the accuracy of the model’s predictions.
Read the rest in the originally published article at: https://itechindia.co/us/blog/guide-to-machine-learning-monitoring-after-deployment/
